Knowledge of the factors influencing nutrient-limited subtropical maize yield and subsequent prediction is crucial for effective nutrientmanagement,maximizing profitability,ensuring food security,and promoting environ...Knowledge of the factors influencing nutrient-limited subtropical maize yield and subsequent prediction is crucial for effective nutrientmanagement,maximizing profitability,ensuring food security,and promoting environmental sustainability.Weanalyzed data fromnutrient omission plot trials(NOPTs)conducted in 324 farmers'fields across ten agroecological zones(AEZs)in the Eastern Indo-Gangetic Plains(EIGP)of Bangladesh to explain maize yield variability and identify variables controlling nutrient-limited yields.An additive main effect and multiplicative interaction(AMMI)model was used to explain maize yield variability with nutrient addition.Interpretable machine learning(ML)algorithms in automatic machine learning(AutoML)frameworks were subsequently used to predict attainable yield relative nutrient-limited yield(RY)and to rank variables that control RY.The stack-ensemble model was identified as the best-performing model for predicting RYs of N,P,and Zn.In contrast,deep learning outperformed all base learners for predicting RYK.The best model's square errors(RMSEs)were 0.122,0.105,0.123,and 0.104 for RY_(N),RY_(P),RY_(K),and RY_(Zn),respectively.The permutation-based feature importance technique identified soil pH as the most critical variable controlling RY_(N)and RY_(P).The RY_(K)showed lower in the eastern longitudinal direction.Soil N and Zn were associated with RYZn.The predicted median RY of N,P,K,and Zn,representing average soil fertility,was 0.51,0.84,0.87,and 0.97,accounting for 44,54,54,and 48%upland dry season crop area of Bangladesh,respectively.Efforts are needed to update databases cataloging variability in land type inundation classes,soil characteristics,and INS and combine them with farmers'crop management information to develop more precise nutrient guidelines for maize in the EIGP.展开更多
文摘Knowledge of the factors influencing nutrient-limited subtropical maize yield and subsequent prediction is crucial for effective nutrientmanagement,maximizing profitability,ensuring food security,and promoting environmental sustainability.Weanalyzed data fromnutrient omission plot trials(NOPTs)conducted in 324 farmers'fields across ten agroecological zones(AEZs)in the Eastern Indo-Gangetic Plains(EIGP)of Bangladesh to explain maize yield variability and identify variables controlling nutrient-limited yields.An additive main effect and multiplicative interaction(AMMI)model was used to explain maize yield variability with nutrient addition.Interpretable machine learning(ML)algorithms in automatic machine learning(AutoML)frameworks were subsequently used to predict attainable yield relative nutrient-limited yield(RY)and to rank variables that control RY.The stack-ensemble model was identified as the best-performing model for predicting RYs of N,P,and Zn.In contrast,deep learning outperformed all base learners for predicting RYK.The best model's square errors(RMSEs)were 0.122,0.105,0.123,and 0.104 for RY_(N),RY_(P),RY_(K),and RY_(Zn),respectively.The permutation-based feature importance technique identified soil pH as the most critical variable controlling RY_(N)and RY_(P).The RY_(K)showed lower in the eastern longitudinal direction.Soil N and Zn were associated with RYZn.The predicted median RY of N,P,K,and Zn,representing average soil fertility,was 0.51,0.84,0.87,and 0.97,accounting for 44,54,54,and 48%upland dry season crop area of Bangladesh,respectively.Efforts are needed to update databases cataloging variability in land type inundation classes,soil characteristics,and INS and combine them with farmers'crop management information to develop more precise nutrient guidelines for maize in the EIGP.
